Inspiration
All of us go to school in Concord, MA, a town known for its local history. Many of these local businesses have taken a hit during the pandemic. In this time of physical isolation and political polarization, mental health issues are on the rise as well. We sought to create a solution to both of these problems.
What it does
Explorantine helps a user decide what locally-run business they can safely visit based on your interests. Based on factors like activity, location, price range, and how the user is feeling, our site will recommend a list of activities gathered by the Google Places API. Given the qualities, the code runs through a multifiltering algorithm that matches the filters with specific locations that match the requirements. If the user prefers an activity outside for COVID safety reasons, the user can tweak that as well. If you’re on a budget, no problem! We have you covered.
The mood feature is a result of the pandemic, because many people do not know what to do to get out of their house given that their normal activities are no longer feasible. People who only have an abstract concept of what they want to do can type their mood into this feature, which correlates with several mood keywords that we have defined using the nltk library, a vector library that correlates words based on meaning.
How we built it
Our tech stack included Python, SQLAlchemy, Flask, spaCy, NLTK (Natural Language Toolkit), Javascript, HTML, CSS, and Bootstrap.
We used Flask to bridge the front-end with various Google Cloud Platform APIs like Places and Geolocation. The backend calculated factors like a user’s current location, which restaurants are closest to them, and which restaurants passed filters depending on price range, closeness, and whether it was indoors or not.
Before, we have not been as comfortable with using the Google Cloud APIs, but it turns out that it was very simple. We just needed a few things: An API key, some formatted parameters, and the Requests module. Problem solved!
We also built our own APIs within our app to allow for the flow of information and an easy conversion from moods to activities to location types to specific places for the user.
Challenges we faced
One obstacle we faced was in hosting our website on glitch and github. Because of the dependencies of our website, the project was either taking up too much memory or too much disk space. We knew that we couldn't do our website justice if we downsized our website to fit the glitch and github, so we reached out to organizers and were able to reach a compromise that still allowed our website to shine in the way it deserves.
Additionally, the Google API has its challenges. Most notably, it caused a problem in distributing our code; the moment we made our code public (with our Google API key), Google notified us that we must take it down. So, our current code requires a new API key to be inserted, and will not run as is.
Lastly, we tried to style and format the website without using an API or framework to do it for us, researching web design font pairs and color palettes to do so. However, we decided that the product with the styles.css edits did not look as good as the one with plain bootstrap, so we decided to keep it for version 2, as a future goal.
Future Goals
In the future, we hope to optimize this algorithm. Currently, it takes a while because we make so many calls to the Google API, but there is an opportunity to reduce the number of calls we make by learning the full capabilities of the Google parameters and key words.
Furthermore, we also hope to add an image for each result listing. We believe this is important because local stores and businesses often have a style that is distinct and unique to the people of this community, and we hope that Explorantine can be a place to take comfort in a shared culture despite challenge.
We also hope to add a feature that allows individuals to connect with their friends to find a common location. Each user can add their specific preferences, and we hope our algorithm can account for all of the people in this group’s preferences to find the most enjoyable activity for all of them.
We also hope to use a user's inputted moods to directly calculate recommended activities.
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